Fault diagnosis of rotating machinery plays a important role for the safety
and stability of modern industrial systems. However, there is a distribution
discrepancy between training data and data of real-world operation scenarios,
which causing the decrease of performance of existing systems. This paper
proposed a transfer learning based method utilizing acoustic and vibration
signal to address this distribution discrepancy. We designed the acoustic and
vibration feature fusion MAVgram to offer richer and more reliable information
of faults, coordinating with a DNN-based classifier to obtain more effective
diagnosis representation. The backbone was pre-trained and then fine-tuned to
obtained excellent performance of the target task. Experimental results
demonstrate the effectiveness of the proposed method, and achieved improved
performance compared to STgram-MFN.

利用声音和振动信号的转移学习方法来解决实际运行场景中训练数据与真实数据之间的分布差异，并通过预训练和微调的骨干结构与基于深度神经网络的分类器相结合，取得了较好的故障诊断性能。